System and method for tire contact patch optimization

ABSTRACT

Systems, methods, and computer-readable storage media for using multi-stage machine learning algorithms to optimize tire contact patches on a vehicle. Vehicle sensors first collect vehicle information for the vehicle, and input that data into a first machine learning algorithm. The first machine learning algorithm then outputs a lateral dimension, a longitudinal dimension, and a diagonal dimension, which together identify a tire contact patch of at least one tire on the vehicle. The operator of the vehicle provides a human preference for how the vehicle operates, and a second machine learning algorithm is executed. The second machine learning algorithm inputs can include the plurality of vehicle information, the first machine learning algorithm outputs, and the human preference, and the second machine learning algorithm outputs can include a desired tire pressure of the at least one tire and an air suspension adjustment for the normal load of the vehicle.

BACKGROUND 1. Technical Field

The present disclosure relates to tire contact patch optimization, andmore specifically to using a multi-phase machine learning process todetermine the optimum tire contact patch for a given circumstance.

2. Introduction

A tire contact patch is the portion of a vehicle's tire which actuallycontacts the ground or a road surface. The tire contact patch can beadjusted by modifying the tire's pressure, tire alignment, camber of thetires, and other factors, all of which affect the wear on the tire, thetire's friction against the ground (needed for both braking andacceleration), and comfort, performance, and/or fuel economy of thevehicle. However, often the tire contact patches are not optimized forthe driver's preferences or for the current driving conditions.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description that follows, and in part will be understood from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media which provide a technical solution to the technicalproblem described. A method for performing the concepts disclosed hereincan include: receiving, at a processor from a plurality of sensors, aplurality of vehicle information associated with a vehicle, theplurality of vehicle information comprising: a vehicle velocity; atleast one tire pressure; a normal load; a road surface; and an inertialmovement unit at a center of gravity of the vehicle; executing, via theprocessor, a first machine learning algorithm, wherein: first machinelearning algorithm inputs comprise the plurality of vehicle information;and first machine learning algorithm outputs comprise a lateraldimension, a longitudinal dimension, and a diagonal dimension, thelateral dimension, the longitudinal dimension, and the diagonaldimension defining a tire contact patch of at least one tire on thevehicle; receiving, at the processor, a human preference for how thevehicle operates; and executing, via the processor, a second machinelearning algorithm, wherein: second machine learning algorithm inputscomprise the plurality of vehicle information, the first machinelearning algorithm outputs, and the human preference; and second machinelearning algorithm outputs comprise a desired tire pressure of the atleast one tire and an air suspension adjustment for the normal load ofthe vehicle.

A vehicle configured to perform the concepts disclosed herein caninclude: a plurality of sensors; a processor; and a non-transitorycomputer-readable storage medium having instructions stored which, whenexecuted by the processor, cause the processor to perform operationscomprising: receiving, from the plurality of sensors, a plurality ofvehicle information associated with the vehicle, the plurality ofvehicle information comprising: a vehicle velocity; at least one tirepressure; a normal load; a road surface; and an inertial movement unitat a center of gravity of the vehicle; executing a first machinelearning algorithm, wherein: first machine learning algorithm inputscomprise the plurality of vehicle information; and first machinelearning algorithm outputs comprise a lateral dimension, a longitudinaldimension, and a diagonal dimension, the lateral dimension, thelongitudinal dimension, and the diagonal dimension defining a tirecontact patch of at least one tire on the vehicle; receiving a humanpreference for how the vehicle operates; and executing a second machinelearning algorithm, wherein: second machine learning algorithm inputscomprise the plurality of vehicle information, the first machinelearning algorithm outputs, and the human preference; and second machinelearning algorithm outputs comprise a desired tire pressure of the atleast one tire and an air suspension adjustment for the normal load ofthe vehicle.

A non-transitory computer-readable storage medium configured asdisclosed herein can have instructions stored which, when executed by acomputing device, cause the computing device to perform operations whichinclude: receiving, from a plurality of sensors, a plurality of vehicleinformation associated with a vehicle, the plurality of vehicleinformation comprising: a vehicle velocity; at least one tire pressure;a normal load; a road surface; and an inertial movement unit at a centerof gravity of the vehicle; executing a first machine learning algorithm,wherein: first machine learning algorithm inputs comprise the pluralityof vehicle information; and first machine learning algorithm outputscomprise a lateral dimension, a longitudinal dimension, and a diagonaldimension, the lateral dimension, the longitudinal dimension, and thediagonal dimension defining a tire contact patch of at least one tire onthe vehicle; receiving a human preference for how the vehicle operates;and executing a second machine learning algorithm, wherein: secondmachine learning algorithm inputs comprise the plurality of vehicleinformation, the first machine learning algorithm outputs, and the humanpreference; and second machine learning algorithm outputs comprise adesired tire pressure of the at least one tire and an air suspensionadjustment for the normal load of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example tire contact patch;

FIG. 2 illustrates an example of a first machine learning modelconfigured to output a tire contact patch;

FIG. 3 illustrates an example of a second machine learning modelconfigured to output optimization parameters;

FIG. 4 illustrates an example of a closed feedback loop;

FIG. 5 illustrates an example method embodiment; and

FIG. 6 illustrates an example computer system.

DETAILED DESCRIPTION

Various embodiments of the disclosure are described in detail below.While specific implementations are described, it should be understoodthat this is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure.

One exemplary, non-limiting, practical application to the technicalproblem noted above is using multiple machine learning algorithms, inseries, to determine (using a first machine learning algorithm) anoptimum tire contact patch for current driving conditions and driverpreferences, then determine (using a second machine learning algorithm)how to modify vehicle parameters to obtain that optimization. Eachrespective algorithm executed by a processor or other computing deviceis based on a distinct machine learning model, where the machinelearning models have been converted to executable code.

Consider the following example. As a vehicle is driving down the road,sensors within the vehicle collect information about the vehicle. Forexample, the vehicle sensors can collect the vehicle velocity, tirepressure(s), road surface type over which the vehicle is currentlytraversing, a normal load for the vehicle, current steering input, andInertial Movement Unit (IMU) at the Center of Gravity (CG) for thevehicle. Other exemplary data which can be collected could include slipdata for the various wheels, braking capacity, angle of ascent/descent,general engine data, road conditions (wet, dry, icy, etc.), and/or anyother data conveyed via the Controller Area Network (CAN) bus within avehicle.

The collected vehicle information can be input into a first machinelearning algorithm executed by a processor within the vehicle. Theoutput of the first machine learning algorithm is the current tirecontact patch for one or more of the tires of the vehicle, output asthree values: a “X” value defining the horizontal/lateral dimension,across the tread of the tire, where the tire is contacting the surface;a “Y” value identifying the vertical/longitudinal dimension, followingthe tread of the tire,” where the tire is contacting the surface; and a“Z” value defining a diagonal dimension across the tire contact patch.If the tire contact patch is a rectangle, with the width and heightcorresponding to the X and Y values, then the Z is the diagonaldimension of that rectangle. If the tire contact patch is an oval, thediagonal value Z may represent the furthest dimension within the tirecontact patch which crosses both the X and Y axes.

The X, Y, and Z values defining the vehicle's current tire contact patchare then used as inputs to a second machine learning algorithm.Additional inputs to the second machine learning algorithm can alsoinclude the vehicle information which were used as inputs to the firstmachine learning algorithm, as well as a driver (or other human being)preference on how the vehicle should be optimized. For example, thedriver could indicate that they desire that tire contact patches of thevehicle be configured to optimize the fuel economy of the vehicle. Otherexamples of driver preferences could be that the tire contact patchesminimize the wear on the tires (thereby optimizing tire wear),optimizing ride comfort, or optimizing vehicle performance for a givenscenario (such as optimizing for cornering versus optimizing for lack ofcornering). While in this example the system is configured to take asingle driver preference, in other configurations the inputs to thesecond machine learning algorithm can also include multiple driverpreferences, or ranked driver preferences.

The second machine learning algorithm then outputs optimal vehicleconfiguration values (such as the optimal tire pressure) based on theinputs provided to the algorithm, as well as signals indicating how toadjust aspects of the vehicle to reach those optimal vehicleconfiguration values. For example, the second machine learning algorithmmay output an optimal tire pressure based on the inputs provided to thealgorithm, as well as a normal load adjustment using air suspension. Inanother instance, the second machine learning algorithm may output anoptimal tire pressure, as well as an adjustment value regarding the tirepressure in a single tire. In yet another instance, the second machinelearning algorithm may output the optimal tire pressure, an adjustmentvalue regarding the optimal tire pressure, and an adjustment to thevehicle's shocks.

In some configurations, where the vehicle is not configured toauto-adjust while operating, the outputs of the second machine learningalgorithm can be presented to the driver or to a technician, who canthen make manual adjustments to the vehicle at their judgment. Suchoutput can, for example, be displayed on the vehicle dashboard, via asmartphone application, or by any other effective vehicle-to-humancommunication mechanism.

In configurations where the vehicle is configured to auto-adjust whileoperating, the outputs of the second machine learning algorithm can beprovided to actuators and other control systems within the vehicle. Theactuators can then adjust the vehicle components according to theadjustment values output by the second machine learning algorithm, andsensors can compare the adjusted component values to the optimal/desiredcomponent values output by the second machine learning algorithm. Forexample, an adjustment to the tire pressure can be sent to a CentralTire Inflation System (CTIS), which in turn inflates or deflates thetire according to the adjustment. A tire sensor can then compare thetire pressure, after the adjustment, to the desired tire pressure outputby the second machine learning algorithm, identify if a discrepancyexists, and re-adjust if such a discrepancy exists. Likewise, if anadjustment to the air suspension is necessary based on the secondmachine learning model, the adjustment output can be sent to airsuspension actuators, then the adjusted air suspension level can bedetected, resulting in additional adjustments if necessary.

As an example of how to train the first machine learning model, avehicle manufacturer or other entity can collect known information(corresponding to the input information of the first machine learningmodel, such as the vehicle velocity, tire pressure(s), road surface typeover which the vehicle is currently traversing, a normal load for thevehicle, current steering input, and the IMU at the CG for the vehicle)and known tire contact patches. This data can be collected from multiplevehicles under multiple conditions, preferably with the amount of datacollected from each vehicle being at least thirty minutes of operation,though the amount of data can vary.

The known data of both the vehicle information and the corresponding,known tire contact patches can be compared via a sensitivity analysis,resulting in correlations between the vehicle information and thecorresponding, known tire contact patches. For example, the sensitivityanalysis can execute models (such as a one-at a time test, aderivative-based local method, regression analysis, variance-basedmethod, screening, scatter plots, etc.) to identify how a giveninput/variable affects the likelihood of a specific condition (such asthe X, Y, Z dimensions) in the tire contact patch being determined. Morespecifically, the system can receive the known vehicle information (suchas the vehicle velocity, tire pressure(s), road surface type over whichthe vehicle is currently traversing, a normal load for the vehicle,current steering input, and the IMU at the CG for the vehicle), anddetermine how they affect the size or shape of the tire contact patch.The correlation outputs of the sensitivity analysis identify thelikelihood of a given variable affecting one or more of the tire contactpatch dimensions (X, Y, and/or Z).

The outputs of the sensitivity analysis, as well the training data, canthen be used by to construct a neural network. For example, thecorrelations and test data associated with the sensitivity analysis canbe input into Python, MatLab®, or other development software configuredto construct neural network based on factor-specific data. Depending onthe specific scenario, users can adjust the neural network constructionby selecting from optimization methods including (but not limited to)the least-squares method, the Levenberg-Marquardt algorithm, thegradient descent method, or the Gauss-Newton method. The neural networkcan make predictions of the tire contact patch size given inputvariables corresponding to the same vehicle information which were usedto train the neural network. The neural network can then be converted tomachine code and uploaded into memory, where upon execution by aprocessor the neural network operates as the first machine learningalgorithm.

The second machine learning model is trained in a similar manner, withperformance of a sensitivity analysis on known input variables and knownoutputs, converting the correlations of the sensitivity analysis into aneural network, and then converting the neural network into machine codeto be executed by a processor as the second machine learning algorithm.However, in training the second machine learning model, examples ofknown inputs can include: (1) the same vehicle information used for thefirst machine learning model, such as the vehicle velocity, tirepressure(s), road surface type over which the vehicle is currentlytraversing, a normal load for the vehicle, current steering input, andthe IMU at the CG for the vehicle; (2) current tire contact patchdimension values X, Y, and Z; and (3) a user's choice for optimization,such as fuel economy, tire wear, ride comfort, and/or performance. Theknown outputs of the second machine learning model, which are used inthe sensitivity analysis, can be the optimal vehicle configurationvalues (such as the optimum tire pressure, shock sensitivity, load,etc.) of the vehicle. These inputs and outputs are subjected to asensitivity analysis, and the resulting correlations are used toconstruct a second neural network in the same manner as described abovewith respect to the first neural network. The resulting neural networkis then converted to machine code to be executed by a processor.

After training both the first machine learning model and the secondmachine learning model, converting the models to code/algorithms, andloading the respective algorithms into memory within a vehicle forexecution, the algorithms can provide feedback on how to adjust thevehicle to best meet the driver's preferences. The sensor data iscollected and input into the processor, such that the processor isiterating new values of the current tire contact patch from the firstmachine learning algorithm. The outputs of the first machine learningalgorithm (the X, Y, and Z dimensions), the same collected sensor data(or a subset thereof), and the user's desired optimization are then fedinto the second machine learning algorithm, which outputs vehicleconfiguration values (such as the optimal tire pressure), as well assignals indicating how to adjust aspects of the vehicle to reach thoseoptimal vehicle configuration values. The outputs of the second machinelearning algorithm can then be displayed or communicated to the driver(or other human being), or transmitted to actuators within the vehiclewhich can automatically initiate the vehicle configuration changesidentified by the second machine learning algorithm. Sensors can thenverify if the changes are satisfactory, and if not continue to initiateadditional changes.

It is noted that a single processor could be executing both the firstmachine learning algorithm and the second machine learning algorithm, ordistinct processors could be executing each respective algorithm.Furthermore, while the examples provided illustrate the first and secondmachine learning algorithms running sequentially, with the secondmachine learning algorithm receiving data from the first machinelearning algorithm, in practice the processor(s) may execute bothalgorithms in parallel/simultaneously, such that the data processed bythe first machine learning algorithm is based on the most recent sensordata collected, and the data processed by the second machine learningalgorithm is based on the previous iteration/output of the first machinelearning algorithm.

Referring now to the figures, FIG. 1 illustrates an example tire contactpatch. This is the portion of the tire contacting the ground or surfaceupon which a vehicle is resting. The tire contact patch has threevariables: the X variable 102, which is the horizontal dimension across(perpendicular to) the tread of the tire; the Y variable 104, which isthe vertical dimension illustrated, which runs parallel to the tread ofthe tire; and the Z variable 106 which is diagonal dimension across thetire contact patch, and which is the furthest dimension within the tirecontact patch which crosses both the X and Y axes.

FIG. 2 illustrates an example of a first machine learning model 204configured to output the variables for a tire contact patch 208. In thisexample, a number of vehicle information/features 202 are collected asthe vehicle is operating (either in motion or stationary). Exemplaryvehicle information/features 202 can include the vehicle velocity, tirepressure(s), road surface type over which the vehicle is currentlytraversing, a normal load for the vehicle, current steering input,and/or the IMU at the CG for the vehicle. The first machine learningmodel 204 receives the input, then outputs (at least) three values 206,such as: one or more “X” values defining the horizontal/lateraldimension, across the tread of the tire, where the tire is contactingthe surface; one or more “Y” values defining the vertical/longitudinaldimension, following the tread of the tire,” where the tire iscontacting the surface; and one or more “Z” values defining a diagonaldimension across the tire contact patch 208. The X, Y, and Z 206 valuesidentify the tire contact patch 208, which can be rectangular or oval inshape.

FIG. 3 illustrates an example of a second machine learning model 304configured to output optimization parameters 306, 308. In this example,inputs to the second machine learning model 304 can include: (1) thesame vehicle information/features 202 which were used as inputs to thefirst machine learning model; (2) the outputs 206, in the form of the X,Y, and Z values, which were output by the first machine learning model;and (3) a user preference 302 or choice, such as an indication from thedriver that they wish to maximize the potential fuel economy of thevehicle, minimize the potential wear on the tires, or maximize thecomfort of the ride. In some configurations, rather than a singlepreference from the user, the user can rank preferences. The secondmachine learning model 304 receives these inputs, then outputs a vehicleconfiguration parameter 306, vehicle adjustments 308, and/or changes todriver behavior which would best fit the inputs. For example, a vehicleconfiguration parameter 306 can be the optimal tire pressure for thevehicle tire(s). An example vehicle adjustment 308 could include anormal load adjustment using air suspension. The vehicle adjustment 308can direct actuators, pumps, or other mechanisms on the vehicle toadjust while the vehicle is in operation. Alternatively, in someconfigurations the vehicle adjustment 308 can be provided to the driver,maintenance personal, or other vehicle user, to manually make theadjustments 308 to obtain the vehicle configuration parameter 306. Insome cases, the second machine learning model 304 can providesuggestions on changes to how the driver controls the vehicle, such asspeed suggestions, to manage the optimum tire contact patch.

FIG. 4 illustrates an example of a closed feedback loop. In thisexample, the load and tire pressure sensors 402 are being used to detectcurrent load and tire pressure. The tire pressure is compared 404against a reference tire pressure 406, and the difference 412 isprovided to a control system 416. Likewise, the normal load is compared408 to a reference normal load 410, with the difference 414 provided tothe control system 416. In this example, the reference tire pressure 406and/or reference normal load can be, for example, outputs from thesecond machine learning model described in FIG. 3 . In otherconfigurations, the reference tire pressure 406 and/or the referencenormal load 410 can be standard or previously determined referencevalues.

The control system 416 receives the differences 412, 414 between thecurrent tire pressure/load output from the sensors 402 and thereferences 406, 410, then determines to which actuator, pump, or othermechanical means on the vehicle to send adjustment signals. In thiscase, the control system 416 identifies an actuator tire inflator 418,and sends a signal to the actuator tire inflator 418 to fill, ordeflate, a tire based on the difference 412 previously detected.Likewise, the control system 416 identifies an actuator for airsuspension 420, and sends a signal to the actuator for air suspension420 to adjust the vehicle's air suspension based on the difference 414previously detected. The actuator tire inflator 418 and/or the actuatorair suspension 420 make the necessary adjustments, thereby affectingconfiguration and performance of the vehicle 422.

The load and tire pressure sensors 402 can then detect the updatedconfiguration of the vehicle 422, the control system 416 can detect ifdifferences 412, 414 continue to exist between actual and desiredvehicle conditions, and the control system can send the actuators 418,420 additional control signals if needed. This feedback loop cancontinue indefinitely, or until a saturation point is reached (that is,until any differences 412, 414 are smaller than a predeterminedthreshold).

FIG. 5 illustrates an example method embodiment being performed by asystem (such as a vehicle) which includes a processor. The methodincludes receiving, at a processor from a plurality of sensors, aplurality of vehicle information/features associated with a vehicle(502). An example (504) of the plurality of vehicle information caninclude: a vehicle velocity, at least one tire pressure, a normal load,a road surface, and an inertial movement unit at a center of gravity ofthe vehicle. The system executes, via the processor, a first machinelearning algorithm (506) wherein: example first machine learningalgorithm inputs comprise the plurality of vehicle information (508);and example first machine learning algorithm outputs comprise a lateraldimension, a longitudinal dimension, and a diagonal dimension, thelateral dimension, the longitudinal dimension, and the diagonaldimension defining a tire contact patch of at least one tire on thevehicle (510).

The system receives, at the processor, a human preference for how thevehicle operates (512), and executes, via the processor, a secondmachine learning algorithm (514). Example second machine learningalgorithm inputs can comprise the plurality of vehicle information, thefirst machine learning algorithm outputs, and the human preference(516), and example second machine learning algorithm outputs cancomprise a desired tire pressure of the at least one tire and an airsuspension adjustment for the normal load of the vehicle (518).

In some configurations, the illustrated method can be further expandedto include modifying the at least one tire pressure to the desired tirepressure, and modifying air suspension of the vehicle according to theair suspension adjustment. In such cases, the modifying of the at leastone tire pressure occurs via a tire pressure actuator, and the modifyingof the air suspension occurs via an air suspension actuator.

In another example, the illustrated method can further include: traininga first neural network by: generating a first sensitivity analysis forthe plurality of vehicle information and known tire contact patches; andforming a first plurality of nodes and a first plurality of neurallayers linking the first plurality of nodes based on the firstsensitivity analysis, resulting in a first trained neural network; thenconverting the first trained neural network to executable code,resulting in the first machine learning algorithm. Likewise, the secondneural network can be trained by: generating a second sensitivityanalysis for the plurality of vehicle information, the first machinelearning algorithm outputs, known tire pressures, and known airsuspension adjustments for the normal load; and forming a secondplurality of nodes and a second plurality of neural layers linking thesecond plurality of nodes based on the second sensitivity analysis,resulting in a second trained neural network. The second trained neuralnetwork can then be converted to executable code, resulting in thesecond machine learning algorithm.

In some configurations, the human preference can be at least one of:maximum fuel economy; minimize tire wear; maximum ride comfort; andmaximum performance.

In some configurations, both the first machine learning algorithm andthe second machine learning algorithm identify multi-dimensionalboundary conditions associated with respective outputs. For example, theneural networks used to generate the first machine learning algorithmand the second machine learning algorithm can identify boundaries with anumber of dimensions equal to the number of input variables to eachrespective model. If, as described above, the inputs to the firstmachine learning algorithm were: a vehicle velocity, at least one tirepressure, a normal load, a road surface, and an inertial movement unitat a center of gravity of the vehicle, then the number of dimensions “n”used to describe boundaries for different X, Y, and Z dimensions for thetire contact patch, would be six. In such an instance, the first machinelearning algorithm would take the six inputs and identify the X, Y, andZ outputs using the six-dimensional boundary condition. Likewise, in thesecond machine learning algorithm, the number of dimensions could bethirteen: the six inputs which are identical to the first machinelearning algorithm; the X, Y, and Z dimensions for the tire contactpatch; and one of maximum fuel economy, minimize tire wear, maximum ridecomfort, or maximum performance. To be clear, this is exemplary only,and different configurations could have more or less boundary dimensionsfor both algorithms.

With reference to FIG. 6 , an exemplary system includes ageneral-purpose computing device 600, including a processing unit (CPUor processor) 620 and a system bus 610 that couples various systemcomponents including the system memory 630 such as read-only memory(ROM) 640 and random access memory (RAM) 650 to the processor 620. Thesystem 600 can include a cache of high-speed memory connected directlywith, in close proximity to, or integrated as part of the processor 620.The system 600 copies data from the memory 630 and/or the storage device660 to the cache for quick access by the processor 620. In this way, thecache provides a performance boost that avoids processor 620 delayswhile waiting for data. These and other modules can control or beconfigured to control the processor 620 to perform various actions.Other system memory 630 may be available for use as well. The memory 630can include multiple different types of memory with differentperformance characteristics. It can be appreciated that the disclosuremay operate on a computing device 600 with more than one processor 620or on a group or cluster of computing devices networked together toprovide greater processing capability. The processor 620 can include anygeneral purpose processor and a hardware module or software module, suchas module 1 662, module 2 664, and module 3 666 stored in storage device660, configured to control the processor 620 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 620 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

The system bus 610 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 640 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 600, such as during start-up. The computing device 600further includes storage devices 660 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 660 can include software modules 662, 664, 666 forcontrolling the processor 620. Other hardware or software modules arecontemplated. The storage device 660 is connected to the system bus 610by a drive interface. The drives and the associated computer-readablestorage media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputing device 600. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangiblecomputer-readable storage medium in connection with the necessaryhardware components, such as the processor 620, bus 610, display 670,and so forth, to carry out the function. In another aspect, the systemcan use a processor and computer-readable storage medium to storeinstructions which, when executed by the processor, cause the processorto perform a method or other specific actions. The basic components andappropriate variations are contemplated depending on the type of device,such as whether the device 600 is a small, handheld computing device, adesktop computer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk660, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 650, and read-only memory (ROM) 640, may also be used in theexemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 600, an inputdevice 690 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 670 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 600. The communications interface 680generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Use of language such as “at least one of X, Y, and Z,” “at least one ofX, Y, or Z,” “at least one or more of X, Y, and Z,” “at least one ormore of X, Y, or Z,” “at least one or more of X, Y, and/or Z,” or “atleast one of X, Y, and/or Z,” are intended to be inclusive of both asingle item (e.g., just X, or just Y, or just Z) and multiple items(e.g., {X and Y}, {X and Z}, {Y and Z}, or {X, Y, and Z}). The phrase“at least one of” and similar phrases are not intended to convey arequirement that each possible item must be present, although eachpossible item may be present.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

We claim:
 1. A method, comprising: receiving, at a processor from aplurality of sensors, a plurality of vehicle information associated witha vehicle, the plurality of vehicle information comprising: a vehiclevelocity; at least one tire pressure; a normal load; a road surface; andan inertial movement unit at a center of gravity of the vehicle;executing, via the processor, a first machine learning algorithm,wherein: first machine learning algorithm inputs comprise the pluralityof vehicle information; and first machine learning algorithm outputscomprise a lateral dimension, a longitudinal dimension, and a diagonaldimension, the lateral dimension, the longitudinal dimension, and thediagonal dimension defining a tire contact patch of at least one tire onthe vehicle; receiving, at the processor, a human preference for how thevehicle operates; and executing, via the processor, a second machinelearning algorithm, wherein: second machine learning algorithm inputscomprise the plurality of vehicle information, the first machinelearning algorithm outputs, and the human preference; and second machinelearning algorithm outputs comprise a desired tire pressure of the atleast one tire and an air suspension adjustment for the normal load ofthe vehicle.
 2. The method of claim 1, further comprising: modifying theat least one tire pressure to the desired tire pressure; and modifyingair suspension of the vehicle according to the air suspensionadjustment.
 3. The method of claim 2, wherein: the modifying of the atleast one tire pressure occurs via a tire pressure actuator; and themodifying of the air suspension occurs via an air suspension actuator.4. The method of claim 1, further comprising: training a first neuralnetwork by: generating a first sensitivity analysis for the plurality ofvehicle information and known tire contact patches; and forming a firstplurality of nodes and a first plurality of neural layers linking thefirst plurality of nodes based on the first sensitivity analysis,resulting in a first trained neural network; and converting the firsttrained neural network to executable code, resulting in the firstmachine learning algorithm.
 5. The method of claim 4, furthercomprising: training a second neural network by: generating a secondsensitivity analysis for the plurality of vehicle information, the firstmachine learning algorithm outputs, known tire pressures, and known airsuspension adjustments for the normal load; and forming a secondplurality of nodes and a second plurality of neural layers linking thesecond plurality of nodes based on the second sensitivity analysis,resulting in a second trained neural network; and converting the secondtrained neural network to executable code, resulting in the secondmachine learning algorithm.
 6. The method of claim 1, wherein the humanpreference comprises at least one of: maximum fuel economy; minimizetire wear; maximum ride comfort; and maximum performance.
 7. The methodof claim 1, wherein both the first machine learning algorithm and thesecond machine learning algorithm identify multi-dimensional boundaryconditions associated with respective outputs.
 8. A vehicle comprising:a plurality of sensors; a processor; and a non-transitorycomputer-readable storage medium having instructions stored which, whenexecuted by the processor, cause the processor to perform operationscomprising: receiving, from the plurality of sensors, a plurality ofvehicle information associated with the vehicle, the plurality ofvehicle information comprising: a vehicle velocity; at least one tirepressure; a normal load; a road surface; and an inertial movement unitat a center of gravity of the vehicle; executing a first machinelearning algorithm, wherein: first machine learning algorithm inputscomprise the plurality of vehicle information; and first machinelearning algorithm outputs comprise a lateral dimension, a longitudinaldimension, and a diagonal dimension, the lateral dimension, thelongitudinal dimension, and the diagonal dimension defining a tirecontact patch of at least one tire on the vehicle; receiving a humanpreference for how the vehicle operates; and executing a second machinelearning algorithm, wherein: second machine learning algorithm inputscomprise the plurality of vehicle information, the first machinelearning algorithm outputs, and the human preference; and second machinelearning algorithm outputs comprise a desired tire pressure of the atleast one tire and an air suspension adjustment for the normal load ofthe vehicle.
 9. The vehicle of claim 8, the non-transitorycomputer-readable storage medium having additional instructions storedwhich, when executed by the processor, cause the processor to performoperations comprising: modifying the at least one tire pressure to thedesired tire pressure; and modifying air suspension of the vehicleaccording to the air suspension adjustment.
 10. The vehicle of claim 9,wherein: the modifying of the at least one tire pressure occurs via atire pressure actuator; and the modifying of the air suspension occursvia an air suspension actuator.
 11. The vehicle of claim 8, thenon-transitory computer-readable storage medium having additionalinstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: training a first neuralnetwork by: generating a first sensitivity analysis for the plurality ofvehicle information and known tire contact patches; and forming a firstplurality of nodes and a first plurality of neural layers linking thefirst plurality of nodes based on the first sensitivity analysis,resulting in a first trained neural network; and converting the firsttrained neural network to executable code, resulting in the firstmachine learning algorithm.
 12. The vehicle of claim 11, thenon-transitory computer-readable storage medium having additionalinstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: training a second neuralnetwork by: generating a second sensitivity analysis for the pluralityof vehicle information, the first machine learning algorithm outputs,known tire pressures, and known air suspension adjustments for thenormal load; forming a second plurality of nodes and a second pluralityof neural layers linking the second plurality of nodes based on thesecond sensitivity analysis, resulting in a second trained neuralnetwork; and converting the second trained neural network to executablecode, resulting in the second machine learning algorithm.
 13. Thevehicle of claim 8, wherein the human preference comprises at least oneof: maximum fuel economy; minimize tire wear; maximum ride comfort; andmaximum performance.
 14. The vehicle of claim 8, wherein both the firstmachine learning algorithm and the second machine learning algorithmidentify multi-dimensional boundary conditions associated withrespective outputs.
 15. A non-transitory computer-readable storagemedium having instructions stored which, when executed by a processor,cause the processor to perform operations comprising: receiving, from aplurality of sensors, a plurality of vehicle information associated witha vehicle, the plurality of vehicle information comprising: a vehiclevelocity; at least one tire pressure; a normal load; a road surface; andan inertial movement unit at a center of gravity of the vehicle;executing a first machine learning algorithm, wherein: first machinelearning algorithm inputs comprise the plurality of vehicle information;and first machine learning algorithm outputs comprise a lateraldimension, a longitudinal dimension, and a diagonal dimension, thelateral dimension, the longitudinal dimension, and the diagonaldimension defining a tire contact patch of at least one tire on thevehicle; receiving a human preference for how the vehicle operates; andexecuting a second machine learning algorithm, wherein: second machinelearning algorithm inputs comprise the plurality of vehicle information,the first machine learning algorithm outputs, and the human preference;and second machine learning algorithm outputs comprise a desired tirepressure of the at least one tire and an air suspension adjustment forthe normal load of the vehicle.
 16. The non-transitory computer-readablestorage medium of claim 15, having additional instructions stored which,when executed by the processor, cause the processor to performoperations comprising: modifying the at least one tire pressure to thedesired tire pressure; and modifying air suspension of the vehicleaccording to the air suspension adjustment.
 17. The non-transitorycomputer-readable storage medium of claim 16, wherein: the modifying ofthe at least one tire pressure occurs via a tire pressure actuator; andthe modifying of the air suspension occurs via an air suspensionactuator.
 18. The non-transitory computer-readable storage medium ofclaim 15, having additional instructions stored which, when executed bythe processor, cause the processor to perform operations comprising:training a first neural network by: generating a first sensitivityanalysis for the plurality of vehicle information and known tire contactpatches; and forming a first plurality of nodes and a first plurality ofneural layers linking the first plurality of nodes based on the firstsensitivity analysis, resulting in a first trained neural network; andconverting the first trained neural network to executable code,resulting in the first machine learning algorithm.
 19. Thenon-transitory computer-readable storage medium of claim 18, havingadditional instructions stored which, when executed by the processor,cause the processor to perform operations comprising: training a secondneural network by: generating a second sensitivity analysis for theplurality of vehicle information, the first machine learning algorithmoutputs, known tire pressures, and known air suspension adjustments forthe normal load; forming a second plurality of nodes and a secondplurality of neural layers linking the second plurality of nodes basedon the second sensitivity analysis, resulting in a second trained neuralnetwork; and converting the second trained neural network to executablecode, resulting in the second machine learning algorithm.
 20. Thenon-transitory computer-readable storage medium of claim 15, wherein thehuman preference comprises at least one of: maximum fuel economy;minimize tire wear; maximum ride comfort; and maximum performance.